MMRCDISCUSSION PAPER SERIES
No. 264

Inside the garbage-can:An orderly decision-making process in disorderly organization
structures
Nobuyuki Inamizu
Manufacturing Management Research Center, University of Tokyo
June 2009

東京大学ものづくり経営研究センター
Manufacturing Management Research Center (MMRC)
Discussion papers are in draft form distributed for purposes of comment and discussion. Contact the author for
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http://merc.e.u-tokyo.ac.jp/mmrc/dp/index.html

Inside the garbage-can:An orderly decision-making process in disorderly organization structures

Nobuyuki Inamizu

Abstract
This study examines Cohen, March, and Olsen’s (1972) garbage-can model, and reveals thatmany subsequent studies have misinterpreted it. This study reveals it is an orderly decisionmaking process that arises from a typical case of garbage-can situation. That is, under
disorderly (unsegmented) organization structures, decision-makers and problems move incluster from one choice opportunity to the next, and as a result, decision styles do not change
depending on timing. In addition, this study shows that this genuine but paradoxical result isobservable in real organizations, providing the office move case of a Japanese venture firm.
This study not only makes the inside of the garbage-can clear but also relates it to the realorganizational phenomena.

Keywords
Decision-making; organized anarchy; garbage-can model; office management; computer

simulation

Inside the garbage-can:An orderly decision-making process indisorderly organization structures

Nobuyuki Inamizu
Manufacturing Management Research Center, University ofTokyo
Abstract: This study examines Cohen, March, and Olsen’s (1972) garbage-can model,
and reveals that many subsequent studies have misinterpreted it. This study reveals it isan orderly decision-making process that arises from a typical case of garbage-cansituation. That is, under disorderly (unsegmented) organization structures, decisionmakers and problems move in cluster from one choice opportunity to the next, and as aresult, decision styles do not change depending on timing. In addition, this study showsthat this genuine but paradoxical result is observable in real organizations, providing
the office move case of a Japanese venture firm. This study not only makes the insideof the garbage-can clear but also relates it to the real organizational phenomena.

Keywords:

Decision-making;organized

anarchy;

management; computer simulation

1

garbage-canmodel;

office1. Introduction
This study examines Cohen, March, and Olsen’s (1972) (CMO) garbage-can model, andreveals that many subsequent studies have misinterpreted CMO’s original model. While many
garbage-can studies refer to CMO’s work as evidence that disorderly (“unsegmented”)
organization structures cause an disorderly decision-making process, this study points out that,in the original model, disorderly organization structures cause an orderly decision-making
process such as “single-packed behaviors” and “single decision style.” In addition, this study
shows that this paradoxical result can be observed in real organizations, providing an officemove case of a Japanese venture firm.
The garbage-can model is proposed by CMO as an extension of organizational decisionmaking theories (March and Olsen 1986). Classical theories of organizational decision-making
emphasize decision-making as rational on the basis of expectations about the futureconsequences of actions. The major criticisms of these theories are excessive time andinformation demands that go beyond human bounded rationality (Simon 1947; March andSimon 1958), and the assumption that all participants in an organization share the same goals,
or that conflict among them can be managed readily (Cyert and March 1963). Althoughbounded rationality and conflict are major phenomena, they do not exhaust the problems
involved in matching theories of decision-making with many empirical observations,especially in organizations characterized by three general properties of organized anarchy:
problematic preferences, unclear technology, and fluid participation. Under organizedanarchies, organizations can be viewed as collections of choice opportunities looking forproblems, problems looking for choice opportunities, solutions looking for problems, anddecision-makers looking for choice opportunities. Decision processes are affected by thetiming of problems, solutions, decision-makers, and choice opportunities, which are assumed
to be independent exogenous streams flowing through organizations.
CMO translate this view into a computer simulation model and examine how decisions are
made under organized anarchies. Based on the simulation model, subsequent studies haveassumed that the garbage-can decision-making process works in such a way that the
combinations of choice opportunities, problems, solutions, and decision-makers change
randomly and, as a result, the decision style of each choice also changes depending on timing(Weick 1979; Hatch 1997; Scott 2003; Daft 2004). They considered “unsegmented” structures,in which problems, decision-makers, and solutions can move freely among choice2

opportunities as a condition for that process, i.e., a disorderly decision-making process
emerges from disorderly structures.
To tell the truth, the subsequent studies, including CMO, have not examined well theoriginal simulation model, and have not developed new models replacing the original model.
Certainly, CMO described the simulation results well, using many output numerical data suchas problem latency and activity, but they did not examine the simulation process directly.
Analyzing the simulation results only by output numerical data often leads to misreading of
the real simulation processes.
Therefore, this study rebuilds and examines the original CMO model, making its
simulation process visible. As shown in the following, the simulation results of CMO model
are paradoxical. Decision-makers and problems move together in a single pack under
disorderly (“unsegmented”) structures, and decisions are made using only one decision stylewithout any regard to timing. That is, the combinations of problems and decision-makers do
not change, and as a result, decision styles also do not change depending on timing. Thisdecision process can be considered to be orderly; that is, an orderly decision-making process
emerges from disorderly organization structures.
This study also shows that the paradoxical simulation results are observable in realorganizations. This study provides an illuminative case of a Japanese venture firm
characterized by a flattened organization structure and its large nonterritorial office, in which
no one has a specific desk and there are no partitions. While this situation would make thework structure unsegmented, the employees rather gathered and sat in clusters than moved
around and discussed various others in that office. Furthermore, these cohesive clusters
seemed to leave most of the heavy problems unsolved without any regard to timing. This study
not only makes the inside of the garbage-can clear but also relates it to the real organizational
phenomena.

2. Garbage-can model and its widely accepted view
2.1 The original garbage-can model
Garbage-can model is a model of decision-making in organized anarchies; that is, insituations that do not meet the conditions for more classical models of decision-making inthree important ways. One is problematic preferences: the inconsistent and ill-defined
preferences that decision-makers often possess. As CMO noted, decision-makers are as likely3to discover their goals through action as they are to understand them prior to choice. Second,
organized anarchies have unclear technology. People have only a loose understanding ofmeans and ends. Organizational participants gain knowledge by trial-and-error learning, but
without clear understanding of underlying causes. Third, organized anarchies are characterized
by fluid participation. Decision-making participants come and go from the decision process,with their involvement depending upon their energy, interest and other demands on their time.
Therefore, anticipating who will actually be involved in a decision is difficult.
Therefore, the garbage-can model describes the accidental or random confluence of four
streams: 1) choice opportunities, 2) problems, 3) solutions, and 4) decision-makers. Choice
opportunities are occasions when an organization is expected to produce behavior that can becalled decision, such as contract meetings, budget committees, and compensation decisions.
They arise regularly and collect problems, decision-makers, and solutions. Problems areconcerns of people inside and outside the organization. They may involve such things as
logistics, resource allocation, or scheduling. They may involve issues of lifestyle, fairness, orcorrectness. They may involve conflicts among participants or between them and outsiders.
Solutions are answers to problems that may or may not have been recognized. Decisionmakers devote their energies to making choices. They are involved in one choice opportunity
at any one time, but they move from one choice opportunity to another. Decision-making
occurs in a stochastic meeting of choices looking for problems, problems looking for choices,
solutions looking for problems to answer, and decision-makers looking for something todecide.
CMO translate the situations of decision-making into a computer simulation model andexamine how decisions are made. The image of the simulation model is as follows: Choice
opportunities are viewed as garbage cans in the model. Various kinds of problems, energies,
and solutions are dumped by participants into choice opportunities, as if they threw garbage
into cans. When the total energy in a choice opportunity exceeds the requirements to solve theproblems there, the choice is made, as if a full garbage can were cleared out.
Figure 1 illustrates the flow chart of the simulation model. The model has four fixedparameters: (1) number of choice opportunities, 10; (2) number of decision-makers, 10; (3)number of problems, 20; and (4) the solution coefficients for the 20 time periods, 0.6 for eachperiod. The solution coefficient defines the effective energy devoted to a choice opportunity
by the decision-makers.
4

StartRead fixed parameters：
1) The number of choice opportunies (10),problems (20), and decision-makers (10),2) Solution coefficient (0.6)

Set as T = 1 (“T” represents periods).

Initial setting of Choice opportunities
2) Energy Requirement (ER)=1.1 (0.0),
3) Effective Energy (EE)=0.0

A choice opportunity and two problems randomly
become activated (*1).

Allocate
1) decision-makers2) activated problems
to activated choice opportunities, following“access-structure,” “decision-structure” and“allocation assumption (*2).”

Initial setting of Problems2) Access-structure,3) LoadInitial setting of Decision-makers
1) Decision-structure,2) Energy

Calculate total of “ER” in each choice opportunity.
ER = (the number of problems in the choice) × (load)

Calculate total of “EE” in each choice opportunity.
EE = (the number of decision-makers in the choice) × (energy) × (solution coefficient)+ (the carry-over EE from the previous periods)＋

ER − EE

0 or −ER > 0The choice remains activated.
The problems remain activated.
The EE is carried over.

Yes

Decision by
Resolution

Yes

Decision by
Oversight

No
ER at T − 1 = 0
NoDecision by FlightNo

Did you execute all Choice
opportunities?
Yes
T=T + 1Yes

T ≦ 20?
No
Exit*1: The model consists of 20 periods of time. All choice opportunities and problems emerge in the first 10 periods, and no new ones are
introduced in the last 10 periods.
*2: In unsegmented structures, each problem and decision-maker has access to all choice opportunities. Therefore, from all activated choice
opportunities, decision-makers and activated problems choose the one closest to a decision.
*3: At the first period or in the case of the choice appearing newly, "ER at T - 1" is zero.

Figure 1: The flow chart of CMO simulation model.
5The model has two organization structures: “access-structure” and “decision-structure.”
Access or decision structures are a list of choice opportunities to which the problem ordecision-maker “has access.” These structures are of three types: (1) Unsegmented (eachproblem or decision-maker has access to all choice opportunities), (2) specialized (eachproblem or decision-maker has access to only one choice opportunity), and (3) hierarchical(the number of accessible choice opportunities differs depending on the organizational level;
the higher the level, the more accessible choice opportunities there are). This study focuses on
unsegmented structures because this type of structures, as noted later, has attracted most of theattention in the literature.
The simulations are conducted according to the following rules:
1) One choice opportunity and two problems are randomly chosen and activated. The model
consists of 20 periods of time. Therefore, all choice opportunities and problems emerge inthe first 10 periods, and no new ones are introduced in the last 10 periods.
2) Decision-makers and activated problems are attached to activated choice opportunities.
There are no structural limits in unsegmented organization structures. Therefore, from allactivated choice opportunities, they choose the one closest to a decision. As explained
below, when the effective energy (EE) exceeds the energy requirement (ER), decisions are
made. Thus, problems and decision-makers enter the choice in which ER − EE is minimal.
This assumption is called “allocation assumption.”
3) The ER (energy requirement) of each choice opportunity is calculated based on problem
load (the amount of energy required to solve).
ER = (the number of problems in the choice) × (load)4) The EE (effective energy) of each choice opportunity is calculated based on decisionmaker’s energy (ability to solve problems) and solution coefficient.
EE = (the number of decision-makers in the choice) × (energy) × (solution coefficient)+ (the carry-over EE from the previous periods)
5) CMO assume that a decision is made whenever the decision-makers present at a choice
opportunity (aided by whatever solutions are available) have enough energy to overcome
the load of problems that are present. More specifically, if ER–EE is equal to or less thanzero, the choice is decided. However, if ER–EE is greater than zero, the result is the
opposite. In this case, the choice and the problems there remain activated, and EE iscarried over to the subsequent periods.
66) By their definition, decision does not always mean “resolution,” and there exist“oversight” and “flight” as the other types of decisions.
Resolution: There are problems associated with a choice opportunity, and the decisionmakers attached to the choice bring enough energy to meet the demands of those
problems. The choice is made and the problems are resolved. More specifically, if EE >ER > 0, the decision is made by “resolution.”
Oversight: Sometimes a choice opportunity arrives and no problems attach themselves tothe choice. All the problems in the system are attached to other choices. In this situation,a choice is made with minimum time and energy. It resolves no problems. Morespecifically, if ER in the present period and ER in the previous period = 0 (however, inthe first period, or in the case of a new entry choice, this equals the initial setting of ER),the decision is made by “oversight.”
Flight: Sometimes a number of problems have been associated with a choice opportunity
for some time. Since they collectively exceed the energy of the decision-makers attachedto the choice, the choice is not made. When another choice opportunity becomes
available, the problems leave the initial choice to attach themselves to another. After the
problems are gone, the original choice is made. It resolves no problems. More specifically,
if ER in the present period = 0 and ER at the previous period > 0, the decision is made by“flight.”

2.2 Widely accepted view on the garbage-can model
CMO translated the ideas noted above into an explicit simulation model, and examined thedecision-making behavior using many output numerical data. However, their simulation
results are so complicated and confusing that the subsequent studies, including that of theoriginal authors, have referred mainly to the following two implications. First, decisionmakers, problems, and solutions move around choice opportunities, and their combinations
change randomly in the decision-making process (“random behaviors”). Second, decisions are
made depending on timing and, therefore, decision styles randomly change over time(“random decision styles”). Decisions are sometimes made by resolution, but also by oversightor flight. As CMO state, “A major feature of the garbage can process is the partial uncouplingof problems and choices. Although decision-making is thought of as a process for solving
problems, that is often not what happens. Problems are worked upon in the context of some7

choice, but choices are made only when shifting combinations of problems, solutions, anddecision-makers happen to make action possible. Quite commonly this is after problems haveleft a given choice arena or before they have discovered it (decisions by flight or oversight)”(p.16).
Besides, subsequent studies by the original authors insist that this decision-making process
can typically be observed in unsegmented structures. As Cohen et al. (1976) state, “Insituations in which load is heavy and the structure is unsegmented, intention is lost in thecontext-dependent flow of problems, solutions, people, and choice opportunities” (p. 37). As
stated by March and Olsen (1986), “In the absence of structural constraints within a garbagecan process, solutions are linked to problems, and decision-makers to choices, primarily by
their simultaneity” (p. 17). March and Olsen (1989) state, “in purest garbage-can situation, we
assume that any problem and any decision-maker can be attached to any choice” (p. 13).
Standard textbooks on organizational theory reflect the implications stated by the original
authors. These textbooks often use the words “random” and “disorderly” to explain the
garbage-can decision-making process (Weick 1979; Hatch 1997; Scott 2003). As Daft (2004)
notes,
With the concept of four streams, the overall pattern of organizational decisionmaking takes on a random [italics added] quality. Organization decisions are
disorderly [italics added] and not the result of a logical step-by-step sequence.
Events may be so ill-defined and complex that decisions, problems, andsolutions act as independent events. When they connect, some problems are
solved, but many are not. (p. 467)
Many academic articles and case studies regard the garbage-can decision-making process
as disorderly. Kingdon (1984) assumes that the combinations of the four elements will change
randomly: “The solutions and problems that come to the fore might change from one meeting
to the next, as given participants attend or fail to attend” (p. 86). Although studies casting
doubt on the garbage-can decision-making process have been published since the late 1980s(Pinfield 1986; Levitt and Nass 1989; Mezias and Scarselletta 1994), they share the view thatthe garbage-can decision-making process itself illustrates the disorderly association of the four
elements. Strategic decision studies also consider the garbage-can model as the mostanarchical and fluid mode of decision-making (Eisenhardt and Zbaracki 1992; Das and Teng
1999). Many academic studies also assume that unsegmented structures cause this disorderly
8decision-making process (e.g., Grandori 1984). Padgett (1980) tries to accommodate thegarbage-can model in hierarchical organizations, and this idea itself shows that many studies
assume that the garbage-can decision-making process can be typically observed in
unsegmented structures.
In sum, the widely accepted view of the garbage-can model is that unsegmented(disorderly) structures lead to a disorderly decision-making process, which is defined by“random behaviors” and “random decision styles.”To tell the truth, the subsequent studies, including CMO, have not examined well theoriginal simulation model, and have not developed new models replacing the original model.
Certainly, CMO described the simulation results well, using many output numerical data suchas problem latency and activity, but they did not examine the simulation process directly.
Generally speaking, multiple simulation runs of the same model may differ from each other,
due to differences in initial conditions and stochastic events, and the results are, therefore,
often path-dependent. To understand the results often requires understanding the details of thehistory of a given run (Axelrod 1997). Analyzing the simulation results only by output
numerical data is more likely to misread the real simulation processes. Therefore, this study
rebuilds and examines the original CMO model, making its simulation process visible. As
shown in the following, the simulation results of CMO model are paradoxical. Besides, theparadoxical result is observable in real organizations.

3. Simulation results
3.1 Verification analysis
We can reconstruct the simulation model precisely because CMO state the source code in
the appendix. CMO also state the random number they used and the order in which the actionsof problems and decision-makers are simulated, we must be able to obtain results identical to
CMO. To examine this, I used the measures of “problem activity (the total number of time
periods a problem is activated and attached to a choice, summed over all problems),”“decision-maker activity (the total number of instances any decision maker shifts from one
choice to another),” and “decision difficulty (the total number of time periods during which a
choice is activated, summed over all choices).” This is because 1) CMO describe thecomputational algorithms of these measures in the source code, and 2) they accurately report
the results of these measures.
9

Table 1 shows the numerical equivalence between CMO and the reconstructed model. The
differences in detail are due to rounding errors. I reconstructed the simulation model using
Fortran 90, Microsoft Excel 2003, and so on, and corrected rounding errors. As a result, I
obtained the identical results, respectively. Therefore, we can conclude that the simulation
model was reconstructed precisely.

Table 1: The precision of the reconstructed model
Measures
Mean problem activity
Light load
Moderate load
Heavy load
Mean decision-maker activity
Light load
Moderate load
Heavy load
Mean decision difficultyLight load
Moderate load
Heavy load

CMO

Reconstructed model

Corrected model

114.9
204.3
211.1

114.8
201.3
210.0

109.1
192.5
225.3

60.9
63.8
76.6

61.0
66.0
76.9

62.0
78.2
65.7

19.5
32.9
46.1

19.5
34.1
46.1

18.3
42.6
36.5

Note: The corrected model corrects the initial value of energy requirement (ER) of choice
opportunities from 1.1 to 0.0.

However, I came across a strange setting when examining the source code. The initialvalue of Energy Requirement (ER) of choice opportunities was set at 1.1 as if there were
problems in the choice opportunity from the beginning. Then, I corrected the value from 1.1 to
0.0. Table 1 shows the results of this corrected simulation model. Despite the great differencesfrom CMO at some points, we should consider these results as true ones.

3.2 Output data analysis
Table 2 shows the number of each decision style under unsegmented structures and eachproblem load. In CMO model, there are three types of energy distribution of decision-makers,
and four combinations of problem and choice opportunity entry times. Therefore, there are 12
types of parameter setting for each problem load. Because one simulation run consists of 10
choice opportunities, 120 in total emerge. As shown in Table 2, every decision style isresolution under light and moderate load, but is flight under heavy load. This result isrelatively robust to the amount of decision-makers’ energies and solution coefficient. In the10simulation, the equation deciding whether a choice is made or not is as follows.
(The number of problems) × (load) ≤(The number of decision-makers) × (energy) × (solution coefficient) + (the carry-overenergies)
Load, energy, and solution coefficient no more than affect the different side of the equation.
Therefore, each parameter setting shows almost the same result. In sum, it is not “random
decision styles” but “single decision style,” which indicates all choices are made by only one
decision style, to be observed in unsegmented organization structures.

Table 2: The number of each decision style in unsegmented organization
structures.
Resolution
Light load
120
Moderate load
120
Heavy load0Oversight000Flight00108

Note: There are three types of energy distribution of decision-makers, and four combinationsof problem and choice opportunity entry times. Therefore, there are 12 types of parametersetting for each problem load. Because one simulation run consists of 10 choice opportunities,
120 in total become activated. All choices are made by resolution under light and moderateload. One-tenth of choices are not made and all the others are made by flight under heavy load.

3.3 Simulation process analysis
Figure 2 shows a simulation process under the light load. I scaled down the figure by half
for simplification: Five choice opportunities, ten problems, five decision makers, and ten
periods. The essence of the results does not change in spite of the scaling down. In this figure,inverted trapezia are choice opportunities, and circles and squares in inverted trapezia
represent problems and decision-makers respectively.
Choice opportunity “3” (C3) and problem “4” (P4) and “9” (P9) become activated at thefirst period. C3 is the sole activated choice and is accessible for all decision-makers andproblems. Thus, all decision-makers and activated problems (P4 and P9) enter C3. Because theeffective energy EE is greater than the energy requirement ER (>0), C3 is made by resolution.
Then, the simulation moves on to the next period. C2, P0, and P7 become activated. In the11same way as in the previous period, all decision-makers and activated problems (P0 and P7)
enter C2. EE is greater than ER at C2, and the choice is made by resolution. In this way, alldecision-makers gather at the new choices and resolve new problems at once.
Figure 3 shows a simulation process under the heavy load. C3, P4, and P9 become
activated at the first period. P4, P9, and all decision-makers enter C3. The decision-makers
reach the next period without a decision because the problems are too heavy to solve (ER >EE). C2, P0, and P7 become activated at the second period. Because the initial values of ER
and EE of the new choice C2 are equal to zero, this choice is closest to a decision (ER − EE =0). Moreover, C2 is accessible for all decision-makers and problems. Thus, all decisionmakers and activated problems (P0, P4, P7, and P9) enter C2. C2 is not made because ER(loads of P0, P4, P7, and P9) is greater than EE (energies of all decision-makers). The oldchoice C3, however, is made because it has no problems (EE ≥ ER = 0). Its decision style isflight. ER at the first period is greater than zero because there were the problems P4 and P9.
All decision-makers and all activated problems move in clusters from a choice to another newone, depending on the allocation assumptions. The old choices are always made by flightbecause all problems have left the choices. In sum, it is not “random behaviors” but “singlepacked behaviors,” which indicates problems and decision-makers move together from choice
to choice, to be observed in unsegmented organization structures.

12Choice opportunity 1

Choice opportunity 2

Choice opportunity 3

Period 1
4

9

Choice opportunity 4

Choice opportunity 5

4
2 3
0 1

Resolution4
2 3

Period 207

0 1

Resolution4
2 3

Period 3
30 57

0 1

Resolution
4

Period 4
2

8

2 3
0 1

Flow

Resolution4
2 3

Period 5
1

6

0 1

Time

Resolution
Period 6

Period 7

Period 8

Period 9

Period 10

Inactive choice opportunity

Active choice opportunity
Problems 5 and 8 are inside.
5

8

Decided choice opportunity

3

Decision-maker 3 is inside.
Already decided choice opportunity

The decision type is indicated.
5

8

3

Resolution

Figure 2: A simulation process under unsegmented organization
structures and light load.
13

Choice opportunity 1

Choice opportunity 2

Choice opportunity 3

Period 1
4

Period 2

9
0

4

7

9

Choice opportunity 4

Choice opportunity 5

4
2 3
0 1

4
2 3
0 1

FlightPeriod 3

5

7

9

0

3

4

4
2 3
0 1

FlightPeriod 4

8

9

4

5

7

0

2

3

4
2 3
0 1

FlightFlow

9Period 5

6 7

8

3

4

5

0

1

2

4
2 3
0 1

Time

Flight9

Period 6

6 7

8

3

4

5

0

1

2

4
2 3
0 1

9

Period 7

6 7

8

3

4

5

0

1

2

4
2 3
0 1

9

Period 8

6 7

8

3

4

5

0

1

2

4
2 3
0 1

9

Period 9

6 7

8

3

4

5

0

1

2

4
2 3
0 1

9

Period 10

6 7

8

3

4

5

0

1

2

4
2 3
0 1

Inactive choice opportunity

Active choice opportunity
Problems 5 and 8 are inside.
5

8

Decided choice opportunity

3

Decision-maker 3 is inside.
Already decided choice opportunity

The decision type is indicated.
5

8

3

Resolution

Figure 3: A simulation process under unsegmented organization
structures and heavy load.
14

The genuine results of the simulation model are paradoxical. The unsegmentedorganization structures lead to an orderly decision process defined by “single-packed
behaviors” and “single decision style.” “Random behaviors” and “random decision styles” inunsegmented structures may be mere illusions having been created by the subsequent garbagecan studies. To tell the truth, CMO (1972), the original paper of the garbage-can model,
pointed out the orderly decision process, especially “single-packed behaviors.” CMO state as asimulation result, “a typical feature of the model is the tendency of decision makers andproblems to track each other through choices. … both decision makers and problems tend tomove together from choice to choice [italics added]” (pp. 9-10). CMO, however, did not refer“moving together” in their conclusion section (see the quotation stated in the second section ofthis paper). As a result, the subsequent studies have omitted the sentence about “single packed
behaviors” and have left only the sentence “tracking each other.” March and Olsen (1986)
only state, “decision makers and problems tend to track one another through choices” (p.18),
as a feature of the unsegmented structures. March (1994) only states, “decision makers,
problems, and solutions tend to track each other through the system” (p.202), as well.
Therefore, separated from “moving together,” “tracking each other” has become interpreted
unconsciously as the “random behaviors,” and this misinterpretation has spread. This might
not have occurred if CMO had examined not only the numerical simulation results but also thesimulation process as this study did.

4. A case of disorderly (unsegmented) organization structures
In this section, we will show that the genuine but paradoxical simulation results areobservable in real organizations, providing an illuminative case. This case study investigates aJapanese venture firm in the telecommunications industry, which we refer to as “X.” Its main
business is system and application program development for cellular-based internet services.
This company has no formal organizational chart. The employees are able to get together anddisperse flexibly during each project. The company has offices in Tokyo and Kyoto; research
for the study was conducted in the Tokyo office, which is a nonterritorial office where no one
has a particular desk and almost no visible partitions exist. This office setting would makework structures more unsegmented. Some studies have assumed that, in this type of office,each employee’s neighbors change from time to time, and employees tend to communicate
with their numerous neighbors, especially those with whom they are relatively unacquainted
15

(Allen and Gerstberger 1973; Allen 1977; Sundstrom and Sundstrom 1986). This suggests“random behaviors” in the garbage-can simulation model. As shown in this section, however,“single-packed behaviors” can be observed in this office arrangement.
Nearly 60 percent of the employees in the office are engineers; the others are engaged insales and administrative roles. Most engineers come to the office every day and spend most of
their time in the office. They design, develop, and improve systems and programs throughface-to-face communications. Of course, the office had advanced IT capabilities, and theengineers interacted by e-mail or other electronic medium. However, they usually decided
important issues at face-to-face meetings.
The number of employees at the Tokyo office had risen to about 50 by October 2004.
Because this number exceeded the capacity of the office, the freedom of movement of theemployees was restricted at that time. Thus, in November 2004, the company moved to a new,large, and nonterritorial office.

4.1 Data collection
The Japanese venture firm case study was based on simple questionnaire surveys,
interviews, and observations at the company’s Tokyo office. I conducted questionnaire surveys
2 weeks before the move (mid-October 2004) and again 3 months after the move (end ofJanuary 2005). The questionnaires were identical with 50 yes/no questions on the employees’
attitudes developed by Takahashi (1997a; b) and 15 yes/no questions on the atmosphere within
the office. I administered the premove survey to 51 employees (all the employees) andobtained 48 responses. I administered the postmove survey to 50 employees (all theemployees) and obtained 41 responses. Nearly 86 percent of the respondents were male and 60
percent were engineers. About 60 percent of respondents were regular staff. These proportions
were in line with those of the entire organization. There was also no significant difference
between the proportions in the premove survey and those in the postmove survey. I also
conducted twenty interviews with eleven employees (of about 45 to 60 minutes each) from
September 2004 to November 2005. The interviewees consisted of executives, generalmanagers, managers, and ordinary employees in sales, engineering, and administration. In
these interviews, I asked about their work patterns and attitude in the office. In addition, I
observed their behavior in the office about twice a week from October 2004 to March 2005.
For the analysis, I selected the following items from the questionnaire survey.
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Restrictions: The limited space before the move detracted from the advantage of nonterritorial
offices because the employees could not move around freely and could not interact well withothers. I selected the following two items as an indicator of these restrictions:
R1. There is enough space to gather at once if necessary: 0 = yes, 1 = no.
R2. You find it difficult to stand up and move around in the office: 1 = yes, 0 = no.
Behaviors: We tend to assume that employees move around and interact with others in a large
nonterritorial office. They will consult the members of other working groups and aid otherprojects as circumstances demand. This is considered “random behaviors” because thediscussion partners change depending on timing. I selected the three items as an indicator of
the employees’ behaviors. The higher these items, the more frequent the change of discussion
partners.
B1. The work atmosphere enables you to consult a member of another group (division orunit) about problems in your business: 1 = yes, 0 = no.
B2. Because the work of others can be seen, it is easy to consult them or offer themassistance in a timely way: 1 = yes, 0 = no.
B3. Because the work of others can be seen, you can help them in emergencies: 1 = yes,0 = no.
Decision style: As an indicator of decision style, I selected the item which had been used tomeasure the degree of decisions by flight in Takahashi (1997a; b). The higher this indicator,
the more frequent the decisions by flight. According to the simulation results, decisions byflight must decrease or increase drastically under unsegmented organizational structures.
D1. When you can avoid completing your assigned tasks long enough, they sometimes
become unnecessary: l = yes, 0 = no.

4.2 Unsegmented structures in a large nonterritorial office
The limited space restricted the employees’ movement before the office move. One
employee said that he and his project members had to go to a restaurant outside the officebecause there was very little space to gather in the office. In observations at the time,
employees arriving late at work had few choices regarding where to sit.
The office move, however, removed this restriction. The questionnaire sample was divided
into the premove group (where N = 48) and the postmove group (where N = 41), and the meanvalue of “restrictions” items R1 and R2 was calculated in each group. The mean of R1 and R2
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of the postmove survey shows a significant decrease at the 1 percent and 10 percent levelcompared to the premove survey (Table 3). In fact, the workspace had an area of only 139.50
square meters before the move, but increased to 486.30 square meters after the move. Thislarge office enabled employees to move around and interact with one another easily.
Company “X” was characterized by a flattened organizational structure (a venture firm), a
nonterritorial office, and large office space. There were few structural constraints on theemployees’ movements and interactions. The company built up the unsegmented structures.

Table 3: Comparisons between the premove and the postmove group

Restrictions
R1
R2
Behaviors
B1
B2
B3
Decision style
D1

Pre-move(N = 48)
Mean
SD

Post-move(N = 41)
Mean
SDt-value

.34
.77

.479
.428

.88
.90

.331
.300

6.184**
1.748+

.88
.85
.88

.334
.357
.334

.61
.73
.78

.494
.449
.419

3.003*
1.434
1.183

.55

.503

.55

.504

0.029

Note: ** p < 0.01, * p < 0.05, + p < 0.1

4.3 An orderly decision-making process in a large nonterritorial officeContrary to assumptions that individuals would communicate with a wide range of
employees, the employees in the large nonterritorial office felt that contacts outside their own
project teams had decreased, that is, discussion partners did not change depending on timing.
In fact, the means of “behaviors” items B1, B2, and B3 were 0.88, 0.85, and 0.88 before themove, but they fell to 0.61, 0.73, and 0.78 respectively after the move (Table 3).
The employees had a pronounced tendency to work in close proximity with others
participating in the same project. Project members needed to join forces to handle therequirements of the customers. Their nonterritorial office was convenient in this respect.
Project members could work near the person with whom they wanted to cooperate and could
sit together. If a desk was assigned to each of them, each had to work separately at his or her
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desk. Moreover, they could gather at once due to sufficient space in office. As there was lackof enough vacant spaces for gathering before the move, they had to wait for the conference
room to become vacant before they could convene even a casual meeting. Further, each
employee often fell into line with his or her own project team. For example, in the past,
employees often went to lunch with the members of other project teams. After the move,
however, each group came to work at its own pace. Thus, when they reached a stage where
they could take a break, they went to lunch with their own project members.
Although decisions by flight must have drastically decreased or increased according to thesimulation results, the mean of decision style (D1) remained 0.55, which suggests decisions byflight did not change at all. However, according to the interviews, decisions by flight seemed
to prevail among the cohesive groups. A regular meeting, in which almost all the employees
took part, was held every Monday. The objective of these meetings was to provide a forum inwhich the problems of each project team could be discussed. The agendas were not ad hoc
solutions but fundamental problems and their eventual solutions. For example, trouble with acustomer was discussed. In this case, the participants did not discuss how to explain thesituation and apologize to the customer; rather, they revealed through discussions that thecause of this trouble was accepting an order that was more than they could handle. They thendiscussed solutions such as personnel policies of securing a sufficient workforce andmanagement policies of not receiving impossible orders, in order to ensure that such problems
would never be repeated. These fruitful discussions, however, were not followed by concrete
actions. Because individuals in each project were fighting to meet tight deadlines, the outcomeof the regular meeting was low on their list of priorities and was postponed.
The questionnaire result may be due to the double-meaning of D1. “Assigned tasks” in D1
may not only mean reforming personnel policies and management policies, but also mean
struggling to meet a deadline. Ideas for company-wide policies are longer-term and often
obscure, but approaching deadlines are short-term and often clear, and therefore they neverfade away. As a result, respondents assuming the former case would answer “yes” but thoseassuming the latter case would answer “no.” The question D1 should have been reconsideredin this case.
In sum, the employees tended rather to cluster together in each project than to change
discussion partners depending on the situation. This suggests “single-packed behaviors.” In
addition, though not having been shown in questionnaire survey, the members of cohesive
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clusters seemed to leave fundamental problems unresolved and to be busy with their current
work. Decisions by flight permeated these groups; in other words, “single decision style”
under a heavy load was observed.

5. Discussion and conclusion
In many garbage-can studies, disorderly (unsegmented) organization structures have been
considered to be a typical situation of the garbage-can decision process characterized by“random behaviors” and “random decision styles.” As shown so far, however, the genuine
simulation process of the original garbage-can model is that disorderly (unsegmented)
organization structures cause an orderly decision-making process characterized by “singlepacked behaviors” and “single decision style.” In addition, this paradoxical simulation result
can be observed in real organizations.
We should reconsider how unsegmented organization structures give rise to an orderly
decision-making process. The causal mechanisms of the simulation results are as follows:
Decision-makers and activated problems try to enter the choice closest to a decision dependingon the allocation assumption, and all of them have access to that choice because each of themhas access to all choices. Thus, all of them enter the new choice. As this process is repeated,decision-makers (and problems) move in clusters from one choice to another (single-packed
behaviors). Decisions are made using only one style (single decision style) due to solid
participation. Above all, flights dominate under the heavy load.
Allocation assumption, which is that problems and decision-makers enter the choice
opportunity closest to a decision, plays a critical role. The idea of this assumption is conceived
from Cyert & March’s (1963) “feedback-react decision procedures” (Cohen et al. 1976).
According to Cyert & March (1963), confronting complex and uncertain situation, decisionmakers make decisions by “feedback-react decision procedures,” to avoid uncertainty. They
avoid the requirement that they correctly anticipate events in the distant future by using
decision rules emphasizing short-run reaction to short-run feedback rather than anticipation of
long-run uncertain strategies. To put it briefly, they tend to solve pressing problems rather thandevelop long-run strategies. Considering the production-level decision as an example,
decision-makers may forecast sales and develop some long-run production plans on paper, but
the actual production decisions are more frequently dominated by day-to-day and week-toweek feedback data from inventory, recent sales, and sales staff.
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The cohesive group members of company “X” would have behaved according to theallocation assumption. If the members could not meet the project’s deadline, they would beunable to receive orders from their customer in the future. Thus, they were working hard tomeet close deadlines and were caught up in a continuous stream of short-term, urgent tasks.
They could not afford to spend any time looking deeply at the long-term important problems.
The issues discussed at the regular meetings were so serious that they could not be solved withimprovised solutions alone. As in “feedback react procedures,” they put new choices (i.e.,
urgent tasks) ahead of serious problems.
In situations where “feedback react procedures” were reasonable, the large nonterritorial
office was convenient for members of each project to get together rather than communicate
across projects. The project members could work together in their large nonterritorial office(i.e., unsegmented structures) without depending on timing because they did not have to wait
for conference rooms or spaces’ being vacant. Whenever a situation requiring urgent response
emerged, all members could join forces in an all-out effort to respond. The project members
moved in clusters from one choice to another.
In order to improve our work, however, we must also examine what happens in other typesof organizations. Although many empirical studies on garbage-can model have been conductedin educational organizations such as universities in the U.S. (e.g., March and Olsen 1976), thecase of this study is of a different type of organization (a venture firm in cellular-based internet
services) functioning in different culture (Japan). Therefore, many other possible explanations
can be considered. One is that in a more collectivist culture (Japan), “single-packed behaviors”
are more likely to occur, even if this takes longer in a territorial office. In this regard, however,many cultural studies have not necessarily supported the hypothesis that Japanese are morecollectivistic than Americans (Matsumoto 2000). Second is that the members of profit-orientedorganizations may feel more preoccupied with responses to their customers, and may
cooperate with each other. Besides, members of small groups may be acquainted with eachother and may tend to become more cohesive than those in large organizations. These are notcontrolled in this study, and future research should address how these differences oforganization type affect the result.
We can also consider some variations of the original simulation model. First, the structurallimits on movement of problems and decision-makers to and from choice opportunities can beverified across wide ranges. Thus, the model can be used to explore the consequences of21

garbage-can processes in relatively tightly coupled systems, containing hierarchies anddivision of labor, as well as in less tightly coupled systems that have been the primary focus ofattention so far. Exploration of temporal contexts in highly structured organizations has hardly
begun. Second, it is more natural to consider that choice opportunities and problems continue
to be introduced into an organization. The original model consists of 20 periods of time. All
choice opportunities and problems emerge in the first 10 periods, and no new ones are
introduced in the last 10 periods. Third, the model may be varied by altering the allocation
assumption. It is possible to imagine various rules by which problems are initially attached to
choices and by which they move. The original model assumed movement to the accessible
choice closest to a decision, but it is possible to explore the implications of assuming various
forms of inertia in movement or to increase random elements in movement. Fourth,assumptions about how choices are made can be modified. The model assumes that there areboth energy demands (carried by problems) and energy resources (carried by decision-makers
and solutions), and that choices are made when energy resources exceed energy demands.
These future researches will provide many useful implications.

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